Dimensional Model Schemas- Star, Snow-Flake and Constellation Foundation & Conformed Dimensions and Facts in Data Warehouse Dimensional Model
Knowledge-Base Enterprise Intelligence Data-Warehouse/Mart DW Dimensional Modeling Concepts
Dimensional Modeling vs. Relational Modeling
Dimensional modeling is different from the OLTP normalized modeling to enable analysis and querying through massive and unpredicted queries. Something which is a relational model is ill-equipped to handle.


How Dimensional model is different from an E-R diagram?

  • An E-R diagram (used in OLTP or transactional system) has highly normalized model (Even at a logical level), whereas dimensional model aggregates most of the attributes and hierarchies of a dimension into a single entity.
  • An E-R diagram is a complex maze of hundreds of entities linked with each other, whereas the Dimensional model has logical grouped set of star-schemas.
  • The E-R diagram is split as per the entities. A dimension model is split as per the dimensions and facts.
  • In an E-R diagram all attributes for an entity including textual as well as numeric, belong to the entity table. Whereas a 'dimension' entity in dimension model has mostly the textual attributes, and the 'fact' entity has mostly numeric attributes.

Dimensional modeling is a better approach for Data warehouse compared to standard Data Model.

The dimensional model has a number of important data warehouse advantages that the ER model lacks.

First advantage of the dimensional model is that there are standard type of joins and framework. All dimensions can be thought of as symmetrically equal entry points into the fact table. The logical design can be done independent of expected query patterns. The user interfaces are symmetrical, the query strategies are symmetrical, and the SQL generated against the dimensional model is symmetrical. In other words,

  • You will never find attributes in fact tables and facts in dimension tables.
  • If you see a non-fact field in the fact table, you can assume that it is a key to a dimension table

Second advantage of the dimensional model is that it is smoothly extensible to accommodate unexpected new data elements and new design decisions. First, all existing tables (both fact and dimension) can be changed in place by simply adding new data rows in the table. Data should not have to be reloaded. Typically, No query tool OR reporting tool needs to be reprogrammed to accommodate the change. All old applications continue to run without yielding different results. You can, respectively, make the following graceful changes to the design after the data warehouse is up and running by:

  • Adding new unanticipated facts (that is, new additive numeric fields in the fact table), as long as they are consistent with the fundamental grain of the existing fact table.
  • Adding completely new dimensions, as long as there is a single value of that dimension defined for each existing fact record
  • Adding new, unanticipated dimensional attributes.
  • Breaking existing dimension records down to a lower level of granularity from a certain point in time forward.

Third advantage of the dimensional model is that there is a body of standard approaches for handling common modeling situations in the business world. Each of these situations has a well-understood set of alternatives that can be specifically programmed in report writers, query tools, and other user interfaces. These modeling situations include:

  • Slowly changing dimensions, where a 'constant' dimension such as Product OR Customer actually evolves slowly and asynchronously. Dimensional modeling provides specific techniques for handling slowly changing dimensions, depending on the business environment.
  • Heterogeneous products, where a business such as a bank needs to:
    • track a number of different lines of business together within a single common set of attributes and facts, but at the same time..
    • it needs to describe and measure the individual lines of business in highly idiosyncratic ways using incompatible measures.
Dimensional Model Schemas- Star, Snow-Flake and Constellation Foundation & Conformed Dimensions and Facts in Data Warehouse Dimensional Model
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